Full Report¶
Climate Change: Visualization for the global warming with world temperatures data and Greenhouse Gas Emission Report data in Canada¶
Group Project DATA 601: Fall 2020¶
Group 9 Shahbaz Masih, Yu Nakamura, Shade Oguntoyinbo
Agenda:
- 1. Introduction
- 2. Dataset
- 3. Guiding Questions
- 4. Tasks
- 5. Conclusion
In [50]:
import pandas as pd
import geopandas as gpd
import numpy as np
import datetime as dt
import matplotlib as mpl
import matplotlib.pyplot as plt
import plotly as plotly
import plotly.express as px
import plotly.offline as py
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import ipywidgets as widgets
from ipywidgets import interact, interactive, fixed, interact_manual
import seaborn as sns
py.init_notebook_mode(connected=True)
mpl.style.use('ggplot')
Introduction¶
- Climate Change is a globally acclaimed discussion with different countries assuming responsibilities on varying scale towards a more sustainable future. Some have pledged to reduce their carbon footprint or greenhouse gases (GHG) emission by certain percentages over the foreseeable future. Others have chosen to believe this is a hoax.
- The current warming trend is of particular significance because most of it is extremely likely (greater than 95 percent probability) to be the result of human activity since the mid-20th century and proceeding at a rate that is unprecedented over decades to millennia. Global Climate Change Vital Signs of the Planet, 2020
- As a country, Canada has pledged to meeting our Paris Agreement GHG emission reduction target of 30% below 2005 levels by 2030. Greenhouse Gas sources and sinks: executive summary 2020
- We are investigating this phenomenon to see what trends exist and how temperatures have changed by countries over several years to further understand the effects of climate change.
Data Sets¶
Dataset 1: Climate Change: Earth Surface Temperature Data
Souce: Kaggle- Features world monthly measurement data collection of land surface temperature from the 1750 to 2013
- The dataset is licensed under open data source by Kaggle which allow us to share, adapt and publish its information for non-commercial purpose.
- File format: csv, Structure, tabular form: 4 columns, 577,463 rows (GlobalLandTemperaturesByCountry.csv), 5 columns, 645,676 rows (GlobalLandTemperaturesByState.csv), 7 columns, 239,000 rows (GlobalLandTemperaturesByMajorCity.csv)
- Last updated 2017
- 243 unique countries (GlobalLandTemperaturesByCountry.csv), 241 unique states and 7 countries (GlobalLandTemperaturesByState.csv), 49 unique countries and 100 cities (GlobalLandTemperaturesByMajorCity.csv)
Dataset 2: Greenhouse Gas Reporting Program (GHGRP)
Souce: GHGRP- Features yearly reported data of GHG emission measurement at each facility in Canada from 2004 to 2018
- The dataset is an open government license which enable us to copy, modify, publish, translate, adapt, distribute, or otherwise use the information in any medium, mode or format for any lawful purpose.
- File format: csv, Structure, tabular form: 77 columns, 9,606 rows
- Last updated 2018 Oct
- 2502 unique Facility Names, 1990 unique Facility Location, 13 unique Facility Provinces
In [51]:
# Read "global temperature by State" csv file
GlobalTempState = pd.read_csv("./GlobalLandTemperaturesByState.csv")
display(GlobalTempState.head())
# Read "GHG emissino data in Canada" csv file
GHG = pd.read_csv("./PDGES-GHGRP-GHGEmissionsGES-2004-Present.csv", encoding = "ISO-8859-1", engine='python')
# Change columns name:
GHG = GHG[["Reference Year / Année de référence" , "Facility Name / Nom de l'installation", "Facility City or District or Municipality / Ville ou District ou Municipalité de l'installation", "Facility Province or Territory / Province ou territoire de l'installation", "Latitude", "Longitude", "Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)", "English Facility NAICS Code Description / Description du code SCIAN de l'installation en anglais"]]
GHG = GHG.rename({"Reference Year / Année de référence": "YEAR", "Facility Name / Nom de l'installation":"FacilityName", "Facility Province or Territory / Province ou territoire de l'installation":"State", "Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)":"TotalEmission", "Facility City or District or Municipality / Ville ou District ou Municipalité de l'installation":"FacilityCity", "English Facility NAICS Code Description / Description du code SCIAN de l'installation en anglais":"FacilityCode"}, axis='columns')
display(GHG.head())
#Read "Global Temperature" csv file
GLT = pd.read_csv('GlobalTemperatures.csv')
display(GLT.head())
#Read "Global Land Temperatures By Country" csv file
LTbyC = pd.read_csv('GlobalLandTemperaturesByCountry.csv')
display(LTbyC.head())
#Read "country_codes" csv file
ctry = pd.read_csv('country_codes.csv')
ctry.Country = ctry.Country.str.strip()
ctry = ctry.replace(['Russian Federation'],'Russia')
display(ctry.head())
Guiding Questions¶
- The first question to address will be if the data contains any outliers and missing values which will require preliminary data exploration (Global temperature data has been questioned for its integrity in the past).
- Is there any correlation between major cities (the main center of activities and industries) temperature rise and corresponding state and countries and global temperature rise?
- How has average land surface temperature changed from 1750 to 2013? When dramatic temperature increase was observed?
- Can we confirm the global warning from this dataset?
- Which countries faced the significant temperature change year over year? Any trends by countries? How about Canada?
- How Greenhouse Gas (GHG) emission measured at each facilities station decrease or increased in Canada over years?
- Any difference between province. If so, what is the main factor of its difference?
- Any relationship between GHG emission and average surface temperatures in Canada?
Tasks and Contributions¶
- Each number of task below corrensponds to above number of Guiding Question.
- Shahbaz Masih conducted data wrangling and created visualizations for Guiding Question 1 and 2
- Shade Oguntoyinbo conducted data wrangling and created visualizations for Guiding Question 3 and 4
- Yu Nakamura conducted data wrangling and created visualizations for Guiding Question 5 and 6
Task : Guiding Question 1¶
- To address question guiding question (a), data will be looked at for basic statistics functions like minimum, maximum, quartile, standard deviation, and mean values of each variable to have a general understanding of range of data values and data spread (from standard deviation). Box plots will be plotted to see data distribution and to identify outliers. The outliers from the data will be removed by using inter-quartile range (IQR) score method. Pandas and matplotlib will be used in this task.
In [52]:
GlobalTemperatures = pd.read_csv("GlobalTemperatures.csv")
display(GlobalTemperatures)
GlobalTemperatures.describe()
#checking nan values
print("Number of NaN values for the column dt :", GlobalTemperatures['dt'].isnull().sum())
print("Number of NaN values for the column LandAverageTemperature :", GlobalTemperatures['LandAverageTemperature'].isnull().sum())
print("Number of NaN values for the column LandMaxTemperature :", GlobalTemperatures['LandMaxTemperature'].isnull().sum())
print("Number of NaN values for the column LandMinTemperature :", GlobalTemperatures['LandMinTemperature'].isnull().sum())
print("Number of NaN values for the column LandAndOceanAverageTemperature :", GlobalTemperatures['LandAndOceanAverageTemperature'].isnull().sum())
#removing nan values
GlobalTemperatures = GlobalTemperatures[GlobalTemperatures['LandAverageTemperature'].notna()]
GlobalTemperatures.rename(columns={'LandAverageTemperature': 'GlobalAverageTemperature'}, inplace=True)
GlobalTemperatures.head()
#Reading country tempertature data and checking statistics
GlobalLandTemperaturesByCountry = pd.read_csv("GlobalLandTemperaturesByCountry.csv")
display(GlobalLandTemperaturesByCountry)
GlobalLandTemperaturesByCountry.describe()
#checking nan values
print("Number of NaN values for the column Average Temperature :", GlobalLandTemperaturesByCountry['AverageTemperature'].isnull().sum())
print("Number of NaN values for the column Average Temperature Uncertainty :", GlobalLandTemperaturesByCountry['AverageTemperatureUncertainty'].isnull().sum())
#removing nan values
GlobalLandTemperaturesByCountry = GlobalLandTemperaturesByCountry[GlobalLandTemperaturesByCountry['AverageTemperature'].notna()]
GlobalLandTemperaturesByCountry.rename(columns={'AverageTemperature': 'CountryAverageTemperature'}, inplace=True)
GlobalLandTemperaturesByCountry.head()
#Reading state tempertature data and checking statistics
GlobalLandTemperaturesByState = pd.read_csv("GlobalLandTemperaturesByState.csv")
display(GlobalLandTemperaturesByState)
GlobalLandTemperaturesByState.describe()
#checking nan values
print("Number of NaN values for the column Average Temperature :", GlobalLandTemperaturesByState['AverageTemperature'].isnull().sum())
print("Number of NaN values for the column Average Temperature Uncertainty :", GlobalLandTemperaturesByState['AverageTemperatureUncertainty'].isnull().sum())
#removing nan values
GlobalLandTemperaturesByState = GlobalLandTemperaturesByState[GlobalLandTemperaturesByState['AverageTemperature'].notna()]
GlobalLandTemperaturesByState.rename(columns={'AverageTemperature': 'StateAverageTemperature'}, inplace=True)
GlobalLandTemperaturesByState.head()
#Reading major city tempertature data and checking statistics
GlobalLandTemperaturesByMajorCity = pd.read_csv("GlobalLandTemperaturesByMajorCity.csv")
display(GlobalLandTemperaturesByMajorCity)
GlobalLandTemperaturesByMajorCity.describe()
#checking nan values
print("Number of NaN values for the column Average Temperature :", GlobalLandTemperaturesByMajorCity['AverageTemperature'].isnull().sum())
print("Number of NaN values for the column Average Temperature Uncertainty :", GlobalLandTemperaturesByMajorCity['AverageTemperatureUncertainty'].isnull().sum())
#removing nan values
GlobalLandTemperaturesByMajorCity = GlobalLandTemperaturesByMajorCity[GlobalLandTemperaturesByMajorCity['AverageTemperature'].notna()]
GlobalLandTemperaturesByMajorCity.shape
GlobalLandTemperaturesByMajorCity.rename(columns={'AverageTemperature':'CityAverageTemperature'}, inplace=True)
GlobalLandTemperaturesByMajorCity.head()
#reading GHG data
CanadaGHGData = pd.read_csv("PDGES-GHGRP-GHGEmissionsGES-2004-Present.csv", encoding = "ISO-8859-1", engine='python')
display(CanadaGHGData)
CanadaGHGData.describe()
#grouping the data by year and summing up the total emissions
YearlyCanadaGHGData =CanadaGHGData[['Reference Year / Année de référence', 'Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)']]
YearlyCanadaGHGData.head()
YearlyCanadaGHGData = YearlyCanadaGHGData.groupby(['Reference Year / Année de référence'], as_index=False)['Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)'].sum()
YearlyCanadaGHGData.head()
Out[52]:
In [53]:
#checking outliers by box plots
ax1 = sns.boxplot(data=GlobalTemperatures)
ax1.set_xticklabels(ax1.get_xticklabels(),rotation=90,fontsize=12)
ax1.set_ylabel('Global Tempertures',fontsize=16)
plt.title('Global Temperatures Box Plots',fontsize=20)
plt.show
Out[53]:
In [54]:
#checking outliers by box plots
ax2 = sns.boxplot(data=GlobalLandTemperaturesByCountry)
ax2.set_xticklabels(ax1.get_xticklabels(),rotation=90,fontsize=12)
ax2.set_ylabel('Tempertures',fontsize=16)
plt.title('Global Temperatures By Country Box Plots',fontsize=20)
plt.show
Out[54]:
In [7]:
#checking outliers by box plots
ax3 = sns.boxplot(data=GlobalLandTemperaturesByState)
ax3.set_xticklabels(ax1.get_xticklabels(),rotation=90,fontsize=12)
ax3.set_ylabel('Tempertures',fontsize=16)
plt.title('Global Temperatures By State Box Plots',fontsize=20)
Out[7]:
In [8]:
#checking outliers by box plots
ax4 = sns.boxplot(data=GlobalLandTemperaturesByMajorCity)
ax4.set_xticklabels(ax1.get_xticklabels(),rotation=90,fontsize=12)
ax4.set_ylabel('Tempertures',fontsize=16)
plt.title('Global Temperatures By Major City Box Plots',fontsize=20)
Out[8]:
In [9]:
ax5 = sns.boxplot(data=CanadaGHGData['Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)'])
ax5.set(ylabel='Total Emissions')
#checking outliers by box plots
#ax5 = sns.boxplot(data=CanadaGHGData['Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)'])
#ax5.set_xticklabels(ax1.get_xticklabels(),rotation=90,fontsize=12)
#ax5.set_ylabel('Total Emissions',fontsize=16)
plt.title('Total GHG Emissions Box Plot',fontsize=20)
Out[9]:
In [10]:
sns.distplot(CanadaGHGData['Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)'], kde=False, color='blue', bins=100)
plt.title('Total Emissions', fontsize=18)
plt.xlabel('Total Emissions (tonnes CO2e)', fontsize=16)
plt.ylabel('Frequency', fontsize=16)
Out[10]:
In [11]:
#IQR score method to remove the outliers
GHGData = CanadaGHGData[['Reference Year / Année de référence', 'Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)']]
GHGData.head()
Q1 = GHGData.quantile(0.25)
Q3 = GHGData.quantile(0.75)
IQR = Q3 - Q1
print(IQR)
df2 = GHGData[~((GHGData < (Q1 - 1.5 * IQR)) |(GHGData > (Q3 + 1.5 * IQR))).any(axis=1)]
df2.shape
Out[11]:
In [12]:
#Box plot after removing outliers
ax6 = sns.boxplot(data=df2['Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)'])
ax6.set(ylabel='Total Emissions')
plt.title('Total GHG Emissions Box Plot After outliers removal',fontsize=20)
Out[12]:
Task : Guiding Question 2¶
- A correlation matrix is build to see if there is any any correlation between major cities (the main center of activities and industries) temperature rise and corresponding state and countries and global temperature rise. Both absolute and differential values will be looked at to see any correlations.
- Line plots were also looked at but did not answer the guiding question properly
- Another dataset (cities with states) was used to combine cities data set with states, countries and global data set.
- Datasets were merged together to combine them altogether matching keys and to suit the purpose
In [13]:
#https://raw.githubusercontent.com/datasets/world-cities/master/data/world-cities.csv
citiesdata = pd.read_csv("cities.csv")
citiesdata.head()
#subcountry was renamed to stat
citiesdata.rename(columns={'name': 'City', 'subcountry': 'State'}, inplace=True)
citiesdata.head()
citieswithstates = citiesdata[['City', 'State']]
citieswithstates.head()
#cities and states temperatures data were merged together
GlobalLandTemperaturesByMajorCitywithstate = pd.merge(GlobalLandTemperaturesByMajorCity, citieswithstates, on='City')
GlobalLandTemperaturesByMajorCitywithstate.head()
citiesandstates = GlobalLandTemperaturesByMajorCitywithstate.merge(GlobalLandTemperaturesByState, on=['dt', 'State', 'Country'])
citiesandstates.head()
#cities and states data was merged with countries data
citiestatesandcountries = citiesandstates.merge(GlobalLandTemperaturesByCountry, on=['dt', 'Country'])
citiestatesandcountries.head()
#cities, states and countries data was merged with global data
citiestatescountriesandglobe = GlobalTemperatures.merge(citiestatesandcountries, on=['dt'])
citiestatescountriesandglobe.head()
#checking if there are any null values
citiestatescountriesandglobe.info(verbose=True, null_counts=True)
#calculating delta Temperature for city, state, country and globe
citiestatescountriesandglobe= citiestatescountriesandglobe.sort_values(by =['City', 'dt'] )
citiestatescountriesandglobe['GlobalDeltaT'] = citiestatescountriesandglobe.groupby('City')['GlobalAverageTemperature'].diff(-1) * (-1)
citiestatescountriesandglobe['StateDeltaT'] = citiestatescountriesandglobe.groupby('City')['StateAverageTemperature'].diff(-1) * (-1)
citiestatescountriesandglobe['CountryDeltaT'] = citiestatescountriesandglobe.groupby('City')['CountryAverageTemperature'].diff(-1) * (-1)
citiestatescountriesandglobe['CityDeltaT'] = citiestatescountriesandglobe.groupby('City')['CityAverageTemperature'].diff(-1) * (-1)
citiestatescountriesandglobe.head()
#separating absolute values of temperature for city, state, country and globe
temperaturedata = citiestatescountriesandglobe[['dt', 'City', 'GlobalAverageTemperature', 'CountryAverageTemperature',
'StateAverageTemperature', 'CityAverageTemperature' ]]
temperaturedata.head()
#separating delta temperatures for city, state, country and globe
temperaturechangedata = citiestatescountriesandglobe[['dt', 'City','GlobalDeltaT', 'CountryDeltaT',
'StateDeltaT', 'CityDeltaT' ]]
temperaturechangedata.head()
#Chicago temperature data
ChicagoTempData= temperaturedata[temperaturedata['City'] == 'Chicago']
ChicagoTempChangeData= temperaturechangedata[temperaturedata['City'] == 'Chicago']
#name of cities in the final dataset
citiestatescountriesandglobe['City'].unique()
Out[13]:
In [14]:
fig, ax = plt.subplots(1,2, figsize=(13,7))
ChicagoTempData.plot(x="dt", y=['GlobalAverageTemperature', 'CountryAverageTemperature',
'StateAverageTemperature', 'CityAverageTemperature' ], ax=ax[0])
ChicagoTempChangeData.plot(x="dt", y=['GlobalDeltaT', 'CountryDeltaT',
'StateDeltaT', 'CityDeltaT'], ax=ax[1])
Out[14]:
In [15]:
temperaturedata.corr()
Out[15]:
In [16]:
sns.heatmap(temperaturedata.corr(), annot = True)
Out[16]:
In [17]:
temperaturechangedata.corr()
Out[17]:
In [18]:
sns.heatmap(temperaturechangedata.corr(), annot = True)
Out[18]:
Task 2¶
Results and Discussion¶
- City data had better correlation with corresponding state and country than with global data
- If there higher activity in a major city with more GHG emissions, its effect is transmitting to related state and country but not so strogly globally. (Another data trend between GHG emission and temperatures does not show any correlation between the two)
Task : Guiding Question 3¶
How has average land surface temperature changed from 1775 to 2015? Was any dramatic temperature change observed? Can we confirm the global warming from this dataset?
Note:¶
- This review was carried out skipping every 5 years to fully visualize the changes
- Recognized changes between 7 - 24% from 1775 to 2015
- Dramatic temperature drop seen between 1810 - 1819 (cold decade)
- Dramatic temperature incrrease noticed in 1980, 1940, 2005, 2010 & 2015. Some of these are on record as the warmest years yet
- Increasing land temperature seen across the globe
In [19]:
#Cleaning, preparing and wrangling data
GLT['dt'] = pd.to_datetime(GLT['dt'])
yseries = GLT['dt'].dt.year
mseries = GLT['dt'].dt.month
GLT = pd.DataFrame({'Year' : yseries, 'Month' : mseries, 'LandAvgTemp' : GLT['LandAverageTemperature']})
grouped = GLT.groupby(['Year','Month'])
GLT_sum = grouped.sum()
GLT_sum = GLT_sum.loc[1775:2015]
yrlyLandTemp = GLT_sum.mean(axis=1, skipna=True).mean(level=['Year']).reset_index()
yrlyLandTemp = yrlyLandTemp.rename(columns={0:'LandAvgTemp'})
yrlyLandTemp_20 = yrlyLandTemp.loc[::5]
TotalTemp1775 = yrlyLandTemp_20.loc[(yrlyLandTemp_20.Year==1775), 'LandAvgTemp']
yrlyLandTemp_20['% Change since 1775']=yrlyLandTemp_20.LandAvgTemp.apply(lambda x : ((x-TotalTemp1775)/TotalTemp1775)*100)
display(yrlyLandTemp_20.head())
In [20]:
fig = px.bar(yrlyLandTemp_20, y=yrlyLandTemp_20['% Change since 1775'], x='Year', color='% Change since 1775',
title = "Change in Average Global Land Temperature: 1775 - 2015")
fig.show()
In [21]:
fig = go.Figure()
fig = go.Figure(data=go.Scatter(x=yrlyLandTemp_20['Year'], y=yrlyLandTemp_20['% Change since 1775'],
mode='lines+markers',marker=dict(color='red'), line=dict(color='black')))
fig.update_layout(title_text="Change in Average Global Land Temperature: 1775 - 2015")
fig.update_xaxes(title_text="Year", type='category')
fig.update_yaxes(title_text="% Change since 1775")
fig.show()
Task : Guiding Question 4¶
- Which countries faced the significant temperature change year over year? Any trends by countries? How about Canada? ##### Note:
- Countries reveiwed include:
- the top countries emitting greenhouse gases (carbon dioxide) according to https://www.weforum.org/agenda/2019/06/chart-of-the-day-these-countries-create-most-of-the-world-s-co2-emissions/ - Brazil, Canada, China, Germany, India, Iran, Japan, Mexico, Russia, Saudi Arabia, South Korea and United States
- top green countries according to https://www.conserve-energy-future.com/greenest-countries-earth.php - Denmark, Luxembourg, Switzerland, Norway
- This is not to say that the 12 emitters are not driving green initiatives
- The 2013 data was not used because it's an incomplete year and may skew the data
- This review was carried out from 1852 to 2012 to exclude missing information
In [22]:
#Cleaning, merging and grouping files
LTbyC['dt'] = pd.to_datetime(LTbyC['dt'])
yseries = LTbyC['dt'].dt.year
mseries = LTbyC['dt'].dt.month
LTbyC = pd.DataFrame({'Year' : yseries, 'Month' : mseries, 'AverageTemperature' : LTbyC['AverageTemperature'],
'Country' : LTbyC['Country']})
LTbyC_merged = pd.merge(LTbyC, ctry, on='Country')
LTbyC_merged = LTbyC_merged.groupby(['Year','Code','Country','Month']).sum()
grouped = LTbyC_merged.groupby(['Year','Code','Country']).mean()
grouped.iloc[1752:2012:10]
yearlyLandTemp = grouped.mean(axis=1, skipna=True).mean(level=['Year','Code','Country']).reset_index()
yearlyLandTemp = yearlyLandTemp.rename(columns={0:'AvgTemp'})
#bins = pd.IntervalIndex.from_tuples([(-22,-12),(-12,-2),(-2,2),(2,12), (12,22), (22,32)], closed ='left')
bins = pd.IntervalIndex.from_tuples([(-22,-17),(-17,-12),(-12,-7),(-7,-2), (-2,3), (3,8),(8,13),(13,18),(18,23),(23,28),(28,33)], closed ='left')
yearlyLandTemp['AvgTemp_bin'] = pd.cut(yearlyLandTemp.AvgTemp, bins, ordered =True)
yrlyLandTemp_sort = yearlyLandTemp.sort_values(by=['AvgTemp_bin','Year'], ascending = False)
display(yrlyLandTemp_sort.head())
display(yrlyLandTemp_sort.describe())
In [23]:
fig = px.choropleth(yrlyLandTemp_sort, locations = 'Code',color="AvgTemp_bin", hover_data =['AvgTemp'], hover_name="Country",
title ='Average Land Temperature by Country: 1752 - 2012', projection = 'robinson', height= 800,
animation_frame = 'Year', color_discrete_sequence= px.colors.sequential.thermal)
fig.update_geos(resolution=50)
fig.show()
In [55]:
#Average Land Temperature in Canada: 1833 - 2012
LTbyC = pd.read_csv('GlobalLandTemperaturesByCountry.csv')
display(LTbyC.head())
LTbyC['dt'] = pd.to_datetime(LTbyC['dt'])
yseries = LTbyC['dt'].dt.year
mseries = LTbyC['dt'].dt.month
LTbyC = pd.DataFrame({'Year' : yseries, 'Month' : mseries, 'AverageTemperature' : LTbyC['AverageTemperature'],
'Country' : LTbyC['Country']})
group = LTbyC.loc[LTbyC.Country == 'Canada'].groupby(['Year','Month'])
LTbyCA_sum = group.sum().loc[1833:2012]
CALandTemp = LTbyCA_sum.mean(axis=1, skipna=True).mean(level=['Year']).reset_index()
CALandTemp = CALandTemp.rename(columns={0:'LandAvgTemp'})
CALandTemp_20 = CALandTemp.loc[::10]
TotalTemp1833 = CALandTemp_20.loc[(CALandTemp_20.Year==1833), 'LandAvgTemp']
CALandTemp_20['% change since 1833']=CALandTemp_20.LandAvgTemp.apply(lambda x : ((x-TotalTemp1833)/TotalTemp1833)*100)
display(CALandTemp_20.head())
In [25]:
fig = go.Figure()
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(
go.Bar(x=CALandTemp_20['Year'], y=CALandTemp_20['% change since 1833'], opacity=0.5,
marker_line_color='rgb(8,200,30)', marker_line_width=2, name="Change since 1833 (%)",
yaxis="y2"))
fig.add_trace(
go.Scatter(x=CALandTemp_20['Year'], y=CALandTemp_20['LandAvgTemp'],name="Average Land Temperature (degrees Celsius)"))
fig.update_layout(title_text="Average Land Temperature in Canada: 1833 - 2015")
fig.update_xaxes(title_text="Year", type='category')
fig.update_yaxes(title_text="Average Land Temperature (degrees Celsius)", secondary_y=False)
fig.update_yaxes(title_text="Change since 1833 (%)", secondary_y=True, autorange="reversed")
In [26]:
fig.show()
In [28]:
LTbyC = pd.read_csv('GlobalLandTemperaturesByCountry.csv')
display(LTbyC.head())
LTbyC['dt'] = pd.to_datetime(LTbyC['dt'])
yseries = LTbyC['dt'].dt.year
mseries = LTbyC['dt'].dt.month
LTbyC = pd.DataFrame({'Year' : yseries, 'Month' : mseries, 'AverageTemperature' : LTbyC['AverageTemperature'],
'Country' : LTbyC['Country']})
group_d = LTbyC.groupby(['Year','Country','Month'])
LTbyC_sum = group_d.sum().loc[1852:2012]
GLandTemp = LTbyC_sum.mean(axis=1, skipna=True).mean(level=['Year', 'Country']).reset_index()
GLandTemp = GLandTemp.rename(columns={0:'LandAvgTemp'})
AvgTemp1852 = GLandTemp.loc[(GLandTemp.Year == 1852)]
AvgTemp1852.set_index(['Country'], inplace=True)
#display(GLandTemp)
CAD = AvgTemp1852.loc['Canada', 'LandAvgTemp']
CHN = AvgTemp1852.loc['China', 'LandAvgTemp']
DNK = AvgTemp1852.loc['Denmark', 'LandAvgTemp']
GER = AvgTemp1852.loc['Germany', 'LandAvgTemp']
IND = AvgTemp1852.loc['India', 'LandAvgTemp']
BRZ = AvgTemp1852.loc['Brazil', 'LandAvgTemp']
IRN = AvgTemp1852.loc['Iran', 'LandAvgTemp']
JPN = AvgTemp1852.loc['Japan', 'LandAvgTemp']
LUX = AvgTemp1852.loc['Luxembourg', 'LandAvgTemp']
MEX = AvgTemp1852.loc['Mexico', 'LandAvgTemp']
RUS = AvgTemp1852.loc['Russia', 'LandAvgTemp']
SAU = AvgTemp1852.loc['Saudi Arabia', 'LandAvgTemp']
ROK = AvgTemp1852.loc['South Korea', 'LandAvgTemp']
CHE = AvgTemp1852.loc['Switzerland', 'LandAvgTemp']
USA = AvgTemp1852.loc['United States', 'LandAvgTemp']
#print(AvgTemp1852)
#Create same bar graph above to check the difference between Country.
def assign1852ByCountry(Country):
if Country =='Canada':
return CAD
elif Country =='China':
return CHN
elif Country =='Denmark':
return DNK
elif Country =='Germany':
return GER
elif Country =='India':
return IND
elif Country =='Brazil':
return BRZ
elif Country =='Iran':
return IRN
elif Country =='Japan':
return JPN
elif Country =='Luxembourg':
return LUX
elif Country =='Mexico':
return MEX
elif Country =='Russia':
return RUS
elif Country =='Saudi Arabia':
return SAU
elif Country =='South Korea':
return ROK
elif Country =='Switzerland':
return CHE
elif Country =='United States':
return USA
GLandTemp['LandAvgTemp1852'] = GLandTemp.Country.apply(assign1852ByCountry)
GLandTemp = GLandTemp.dropna()
function = lambda x, y: (x-y)/y*100
GLandTemp=GLandTemp.assign(percent_change_since_1852= lambda x: ((x['LandAvgTemp']-x['LandAvgTemp1852'])/x['LandAvgTemp1852']*100))
display(GLandTemp)
In [29]:
bar = go.Bar()
fig_b = go.FigureWidget(data=bar)
fig_b.update_yaxes(range=[-70,50])
# A list passed to interact() will yield a drop-down interactor
@interact(Country = ['Brazil','Canada', 'China', 'Denmark', 'Germany','India','Iran','Japan','Luxembourg','Mexico',
'Russia','Saudi Arabia','South Korea','Switzerland','United States'])
def update_bar(Country = 'Germany'):
data = GLandTemp.loc[GLandTemp.Country==Country]
#display(data)
data.set_index('Year', inplace=True)
fig_b.update_traces(x=pd.Series(data.index.values), y=data.percent_change_since_1852, opacity=0.4,
marker_line_color='rgb(8,50,107)', marker_line_width=2, name="Change since 2005 (%)")
fig_b.update_layout(title_text="Average Land Temperature by Country: {0}".format(Country))
fig_b.update_xaxes(title_text="Year", type='category')
fig_b.update_yaxes(title_text="change_since_1852 (%)")
fig_b
Tasks : Guiding Question 5 adn 6¶
Data Wrangling¶
To address the guiding question 4 and 5, here is a list of what kind of data wrangling was conducted.
- How Greenhouse Gas (GHG) emission measured at each facilities station decrease or increased in Canada over years?
- Any difference between province. If so, what is the main factor of its difference?
(List of Data Wrangling)
- Compute
Total GHG Emission (kton. CO2 equivalent)by Year, and by State and Year - Compute
Change of GHG Emission since 2005 (%)by Year, and by State and Year - Create
Facility Groupcolumns based onFacilityCode (string)from origirnal data to investigate the impact of Facility business sectors
- Compute
- Any difference between province. If so, what is the main factor of its difference?
(List of Data Wrangling)
- Any relationship between GHG emission and average surface temperatures in Canada?
(List of Data Wrangling)
- Compute Average Land Surface Temperature by Year, and by Season, Year and State
- Merge two dataframe to make a visualization of average land temperature vs. Total GHG Emission
Greenhouse Gas (GHG) Dataset¶
- Loading csv. , Renaming etc.
In [56]:
# Read "GHG emissino data in Canada" csv file
GHG = pd.read_csv("./PDGES-GHGRP-GHGEmissionsGES-2004-Present.csv", encoding = "ISO-8859-1", engine='python')
# Change columns name:
GHG = GHG[["Reference Year / Année de référence" , "Facility Name / Nom de l'installation", "Facility City or District or Municipality / Ville ou District ou Municipalité de l'installation", "Facility Province or Territory / Province ou territoire de l'installation", "Latitude", "Longitude", "Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)", "English Facility NAICS Code Description / Description du code SCIAN de l'installation en anglais"]]
GHG = GHG.rename({"Reference Year / Année de référence": "YEAR", "Facility Name / Nom de l'installation":"FacilityName", "Facility Province or Territory / Province ou territoire de l'installation":"State", "Total Emissions (tonnes CO2e) / Émissions totales (tonnes éq. CO2)":"TotalEmission", "Facility City or District or Municipality / Ville ou District ou Municipalité de l'installation":"FacilityCity", "English Facility NAICS Code Description / Description du code SCIAN de l'installation en anglais":"FacilityCode"}, axis='columns')
for col in GHG.columns:
print(col + ": " + str(GHG[col].dtype))
display(GHG.head())
display(GHG.tail())
display(GHG.describe())
General: Facility Annual GHG Emission distribution by Year¶
Here is the recorded Annual GHG data at each Facility. You can see a lot of variation of GHG Emission level.
In [57]:
fig = px.violin(GHG, y="TotalEmission", x="YEAR", color="YEAR", box=True,
hover_data=GHG.columns)
fig.show()
TotalEmission data are available at each facility who meet the requirement that emit 10 kilotonnes or more of GHGs, in carbon dioxide (CO2) equivalent (eq.) units, per year. Since Government of Canada changed the reporting threshold which was lowered from 50 kt to 10 kt in 2017. For apple to apple comparison over years, facilities with emissions below 50kt were excluded from this trend analysis.
In [58]:
GHGrev = GHG.loc[(GHG.TotalEmission>=50000)] # Exclude the facilities rows < 50 kt.
GHGrev = GHGrev.loc[(GHG.YEAR >= 2005)]
#display(GHGrev.head())
#display(GHGrev.describe())
fig = px.histogram(GHGrev, x="YEAR", color="State")
fig.update_layout(title='Fig.1 Facilitiy Counts by province :Total Emission Gas >= 50 kt')
fig.update_xaxes(type='category', autorange="reversed")
fig.show()
- Compute the Total GHG Emission, change % since 2005 by Year and by Year and State
In [59]:
# Compute Total GHG Emission and Change % since 2005 by Year
AnnualGHG = GHGrev.groupby(['YEAR']).sum().reset_index()
TotalGHG2005 = AnnualGHG.loc[(AnnualGHG.YEAR==2005), 'TotalEmission']
AnnualGHG['% of change since 2005']=AnnualGHG.TotalEmission.apply(lambda x : (x-TotalGHG2005)/TotalGHG2005*100)
display(AnnualGHG)
In [60]:
# Compute Total GHG Emission and Change % since 2005 by Year and State
AnnualGHGState = GHGrev.groupby(['State', 'YEAR']).sum().reset_index()
TotalGHG2005 = AnnualGHGState.loc[(AnnualGHGState.YEAR==2005)]
TotalGHG2005.set_index('State', inplace=True)
AB = TotalGHG2005.loc['Alberta', 'TotalEmission']
BC = TotalGHG2005.loc['British Columbia', 'TotalEmission']
NL = TotalGHG2005.loc['Newfoundland and Labrador', 'TotalEmission']
PE = TotalGHG2005.loc['Prince Edward Island', 'TotalEmission']
NS = TotalGHG2005.loc['Nova Scotia', 'TotalEmission']
NB = TotalGHG2005.loc['New Brunswick', 'TotalEmission']
QC = TotalGHG2005.loc['Quebec', 'TotalEmission']
ON = TotalGHG2005.loc['Ontario', 'TotalEmission']
MB = TotalGHG2005.loc['Manitoba', 'TotalEmission']
SK = TotalGHG2005.loc['Saskatchewan', 'TotalEmission']
NT = TotalGHG2005.loc['Northwest Territories', 'TotalEmission']
#NU = TotalGHG2005.loc['Nunavut', 'TotalEmission'] # No recored of 2005 GHG Emission of State Nunavun
#print(TotalGHG2005)
# Create same bar and line graph above to check the difference between States.
def Assign2005ByState(State):
if State =='Alberta':
return AB
elif State =='British Columbia':
return BC
elif State =='Newfoundland and Labrador':
return NL
elif State =='Prince Edward Island':
return PE
elif State =='Nova Scotia':
return NS
elif State =='New Brunswick':
return NB
elif State =='Quebec':
return QC
elif State =='Ontario':
return ON
elif State =='Manitoba':
return MB
elif State =='Saskatchewan':
return SK
elif State =='Northwest Territories':
return NT
AnnualGHGState['TotalEmission2005'] = AnnualGHGState.State.apply(Assign2005ByState)
#display(AnnualGHGState)
function = lambda x, y: (x-y)/y*100
#AnnualGHGState['% of change since 2005']=AnnualGHGState.apply(lambda x: function(AnnualGHGState.TotalEmission, AnnualGHGState.TotalEmission2005))
AnnualGHGState=AnnualGHGState.assign(Percent_ChangeSince2005= lambda x: ((x['TotalEmission']-x['TotalEmission2005'])/x['TotalEmission2005']*100))
#display(AnnualGHGState)
In [61]:
AnnualGHGState.set_index('State', inplace=True)
display(AnnualGHGState.head())
- Create
Facility Groupcolumns based onFacilityCode (string)from origirnal data to investigate the impact of Facility business sectors.
In [62]:
GHGindustry = GHGrev.groupby(['State', 'FacilityCode', 'YEAR']).sum().reset_index()
GHGindustry['FacilityCode'] = GHGindustry['FacilityCode'].str.lower()
GHGindustry['FacilityGroup'] = pd.np.where(GHGindustry['FacilityCode'].str.contains("oil"),"Oil and Gas",
pd.np.where(GHGindustry['FacilityCode'].str.contains("gas"),"Oil and Gas",
pd.np.where(GHGindustry['FacilityCode'].str.contains("manufacturing"),"Manufacturing",
pd.np.where(GHGindustry['FacilityCode'].str.contains("mining"),"Mining", "Others"))))
display(GHGindustry)
GHGindistryGroup = GHGindustry.groupby(['FacilityGroup', 'YEAR']).sum().reset_index()
display(GHGindistryGroup.head())
Surface Land Temperature Dataset¶
- Load csv.
In [63]:
# Read "global temperature by State" csv file
GlobalTempState = pd.read_csv("./GlobalLandTemperaturesByState.csv")
display(GlobalTempState.head())
display(GlobalTempState.tail())
display(GlobalTempState.describe())
GlobalTempState['dt'] = pd.to_datetime(GlobalTempState['dt'])
for col in GlobalTempState.columns:
print(col + ": " + str(GlobalTempState[col].dtype))
- Compute Annual Average Temperature in Canada
- Compute Seasonal Average Temperature in Canada
In [64]:
# Add Year, Month columns from date.time frame of 'dt'.
GlobalTempState['YEAR']=GlobalTempState['dt'].dt.year
GlobalTempState['MONTH']=GlobalTempState['dt'].dt.month
GlobalTempState=GlobalTempState.loc[(GlobalTempState.Country=="Canada")] # Only the temperature data in Canada will be used for this task.
GlobalTempState = GlobalTempState.drop(['dt', 'AverageTemperatureUncertainty'], axis=1) # Remove redundant columns
def season(Month):
if Month >=3 and Month <= 5:
return "spring"
elif Month >=6 and Month <=8:
return "summer"
elif Month >=9 and Month <=11:
return "autumn"
elif Month ==12 or Month ==1 or Month ==2:
return "winter"
GlobalTempState['Season']=GlobalTempState['MONTH'].apply(season)
display(GlobalTempState)
In [65]:
# Create table by Year, Month and State.
grouped = GlobalTempState.groupby(['YEAR', 'Season', 'MONTH', 'State'])
TempCanada = grouped.sum()
TempCanada=TempCanada.loc[2004:2012]
display(TempCanada.head())
display(TempCanada.tail())
In [66]:
# Compute annual averarge temperature and seasonal temperature for each province.
annualTemp = TempCanada.mean(axis=1, skipna=True).mean(level=['YEAR']).reset_index()
annualTemp = annualTemp.rename(columns={0:'AvgTemp'})
display(annualTemp)
seasonTempState = TempCanada.mean(axis=1, skipna=True).mean(level=['YEAR', 'Season', 'State']).reset_index()
seasonTempState=seasonTempState.rename(columns={0:'AvgTemp'})
display(seasonTempState.head())
Visualization : Guiding Question 5¶
5. How Greenhouse Gas (GHG) emission measured at each facilities station decrease or increased in Canada over years?
- From Fig.1 (Reporting Facility Counts by Year in the above Data Wrangling section), the number of reporting facilities has increased from 330 to 540 over the year 2005 to 2018. While, overall reported total GHG emission was decreased by 2.3% from 2005 (Fig.2).
- The significant decrease of GHG from 2005 to 2009 up to 9% was seen in Fig.2, but the period between 2010 to 2018, the degree of Total GHG Emission has continousuly growed up.
In [67]:
fig = go.Figure()
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(
go.Bar(x=AnnualGHG['YEAR'], y=AnnualGHG['% of change since 2005'], opacity=0.4,
marker_line_color='rgb(8,50,107)',
marker_line_width=2, name="Change since 2005 (%)", yaxis="y2"
))
fig.add_trace(
go.Scatter(x=AnnualGHG['YEAR'], y=AnnualGHG['TotalEmission'], name="Total GHG Emission (kton)"
))
fig.update_layout(
title_text="Fig.2 Total GHG Emission in Canada over years"
)
fig.update_xaxes(title_text="Year", type='category')
fig.update_yaxes(title_text="Total GHG Emission (kton)", secondary_y=False)
fig.update_yaxes(title_text="Change since 2005 (%)", secondary_y=True, autorange="reversed")
fig.show()
5-1. Any Total GHG trend difference over years between provinces in Canada?
- There is a quite large variation of total GHG Emission over years between provinces.
- The facilities located in West Canada Provinces (Alberta, Northwest Teritory, Blitish Columbia, Saskatuan) tend to have a trend that total GHG has increased over years. Especially, it is outstanding increase of GHG emission in Alberta.
- On the other hand, the facilities located in East Canada Provinces (Ontario, Quebec, Nova Scotia, Newfoundland and Labrador, Prince Edward Island) suceeded to reduce the GHG emission continously since 2005 to 2018.
In [68]:
bar = go.Bar()
fig_bar = go.FigureWidget(data=bar)
fig_bar.update_yaxes(range=[-50, 90])
scatter = go.Scatter()
fig_scatter = go.FigureWidget(data=scatter)
# A list passed to interact() will yield a drop-down interactor
@interact(State = ['Alberta', 'British Columbia', 'Newfoundland and Labrador', 'Prince Edward Island', 'Nova Scotia', 'New Brunswick', 'Quebec', 'Ontario', 'Manitoba', 'Saskatchewan', 'Northwest Territories'])
def update_bar(State = 'Northwest Territories'):
data = AnnualGHGState.loc[State]
#print(data)
data.set_index('YEAR', inplace=True)
fig_bar.update_traces(x=pd.Series(data.index.values),
y=data.Percent_ChangeSince2005, opacity=0.4, marker_line_color='rgb(8,50,107)', marker_line_width=2, name="Change since 2005 (%)")
fig_bar.update_layout(title_text="Fig. 3.1 % Change since 2005 over Years: {0}".format(State))
fig_bar.update_xaxes(title_text="Year", type='category')
fig_bar.update_yaxes(title_text="Change since 2005 (%)")
fig_scatter.update_traces(x=pd.Series(data.index.values),
y=data.TotalEmission, line=dict(color='red', width=2), name="Total GHG Emission (kton)")
fig_scatter.update_layout(title_text="Fig. 3.2 Total GHG Emission (kton) over Years : {0}".format(State))
fig_scatter.update_xaxes(title_text="Year", type='category')
fig_scatter.update_yaxes(title_text="Total GHG Emission (kton)")
fig_bar
In [69]:
fig_scatter
Figure 4 is the spatial mapping for the 2018 GHG Emission Reported to Environment and Climate Chagne Canada, which clearly shows that the highest GHG producers has concentrated on the Alberta.
In [70]:
GHG2018 = GHGrev.loc[(GHG.YEAR==2018)]
GHG2018.dropna(subset=['Latitude', 'Longitude'], inplace=True)
GHG2018 = GHG2018.drop(['YEAR'], axis=1)
#hist = px.histogram(GHG2018, x="TotalEmission")
#hist.show()
def colorbin(GHG):
if GHG >=0 and GHG < 100000:
return "Below100k"
elif GHG >=100000 and GHG < 500000:
return "100k to < 500k"
elif GHG >=500000 and GHG < 1000000:
return "500k to < 1000k"
elif GHG >= 1000000 and GHG < 2000000:
return "1000K to < 2000k"
elif GHG >=2000000:
return "Over 2000k"
GHG2018['2018TotalEmission_kton']=GHG2018['TotalEmission'].apply(colorbin)
# display(GHG2018)
In [72]:
fig = px.scatter_geo(data_frame=GHG2018, lat="Latitude", lon = "Longitude",
color="2018TotalEmission_kton",
color_discrete_map={'Below100k':'blue',
'100k to < 500k':'green',
'500k to < 1000k':'yellow',
'1000K to < 2000k':'orange',
'Over 2000k':'red',
},
category_orders={
"2018TotalEmission_kton": [
"Over 2000k",
"1000K to < 2000k",
"500k to < 1000k",
"100k to < 500k",
"Below100k"
]
},
opacity = 0.8,
title ='Fig.4 2018 Facility Greenhouse Gas Emission Reported to Environment and Climate Change Canada',
scope = 'north america',
width = 1100,
height = 700,
size = 'TotalEmission'
)
fig.update_geos(
visible = False,
scope = 'north america',
showland = True,
landcolor = "rgb(212, 212, 212)",
subunitcolor = "rgb(255, 255, 255)",
countrycolor = "rgb(255, 255, 255)",
showlakes = True,
lakecolor = "rgb(255, 255, 255)",
showsubunits = True,
showcountries = True,
resolution = 50,
projection = dict(
type = 'conic conformal',
rotation_lon = -100
),
lonaxis = dict(
showgrid = True,
gridwidth = 0.5,
range= [ -140.0, -55.0 ],
dtick = 5
),
lataxis = dict (
showgrid = True,
gridwidth = 0.5,
range= [ 20.0, 60.0 ],
dtick = 5
),
)
fig.show()
5-2. Why is there Total GHG Emission difference between Provinces? Does the Business Sector have impaacted to the GHG Emission Level?
- Overall, GHG emission reported by Oil and Gas, Mining Business Sectors have experienced a steady increase over years while the GHG emission reported by Manufacturing and others has reduced.
In [73]:
fig = px.line(GHGindistryGroup, x='YEAR', y='TotalEmission', color='FacilityGroup')
fig.update_traces(mode='lines+markers')
fig.update_layout(title='Fig.5 Total GHG Emission by Main Facility Sectors: Oil and Gas, Mining, Manufacturing, Others')
fig.show()
- The sunburst plot (Figure 6) provides the portion of Main Business Secotrs by each provinces.
- We can confirm more than 50% of Reporting Facilities in Alberta are related to the oil and gas Sectors, which explains why continuous total GHG emission over years can be seen.
- Ontario who hold highest populations in Canada, overall the total emission in 2018 is second high following Alberta but Ontario suceeds to decrease Total GHG Emission over years that main business sector is Manufacturing.
- One concludes the high portion of Oil and Gas Business Secotors definitely contribute to release more GHG, which I use this trend to predict "How likely we will achive the target of 30% GHG reduction by 2030 based on Pari's aggreement" with multi-linear regression model for DATA602 Group Project.
In [74]:
GHG2018 = GHGindustry.loc[(GHG.YEAR==2018)]
GHG2018.dropna(subset=['FacilityCode', 'TotalEmission', 'FacilityGroup'], inplace=True)
#display(GHG2018)
fig = px.sunburst(GHG2018, path=['State', 'FacilityGroup', 'FacilityCode'],
values = 'TotalEmission',
color='TotalEmission',
color_continuous_scale='balance')
fig.update_layout(title='Fig. 6 2018 Total GHG Emission (kton) by State and Main Facility Sectors')
fig.show()
#GHG2018.FacilityCode.unique
In [75]:
AnnualGHGState.reset_index(inplace=True)
fig = px.line(AnnualGHGState, x='YEAR', y='TotalEmission', color='State')
fig.update_traces(mode='lines+markers')
fig.update_layout(title='Fig. 7 Total GHG Emission in Canada over years by province')
fig.show()
Visualization : Guiding Question 6¶
6. Any relationship between GHG emission and average surface temperatures in Canada?
- There is no recognaizable trend between GHG Emission and Average Surface Temperatures in Canada (Fig. 8)
- The scatter plot of summer temperature vs. Total emission by State also doesn't show any clear association between two.
- Simply, most people believe high GHG Emission due to human activity would cause the global warnming. But in our dataset, the GHG reduction effect to temperature is uncertain. Since the dataset is only collected in Canada and relatively short term only covered 2005 to 2018, it might be hard to see clear influence between both.
In [76]:
mergeTable = pd.merge(AnnualGHG, annualTemp, on=['YEAR'])
#display(mergeTable)
fig = go.Figure()
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(
go.Scatter(x=mergeTable['YEAR'], y=mergeTable['AvgTemp'], opacity=0.4,
marker_line_color='rgb(8,50,107)',
marker_line_width=2, name="Average Temperature (degC)", yaxis="y2"
))
fig.add_trace(
go.Scatter(x=mergeTable['YEAR'], y=AnnualGHG['TotalEmission'], name="Total GHG Emission (kton)"
))
fig.update_layout(
title_text="Fig. 8.1 Average Temperature and Total GHG Emission in Canada over years"
)
fig.update_xaxes(title_text="Year", type='category')
fig.update_yaxes(title_text="Total GHG Emission (kton)", secondary_y=False)
fig.update_yaxes(title_text="Average Temperature (degC)", secondary_y=True)
fig.show()
mergeTableState = pd.merge(AnnualGHGState, seasonTempState, on=['YEAR', 'State'])
#display(mergeTableState)
test = mergeTableState.query("Season == 'summer'")
fig = px.scatter(test, x="AvgTemp", y="TotalEmission", color="State", size = "TotalEmission", hover_data=None, title = "Fig. 8.2 Average Temperature of Summer and Total GHG Emission by Procince")
fig.show()
Conclusion¶
We succeed to address the guiding question with Python functions: plotly express, Numpy through data wrangling and visualizations.
- nan values were deleted from the datasets. Outliers were checked for all data columns and removed accordingly.
- City data had better correlation with corresponding state and country than with global data
- If there higher activity in a major city with more GHG emissions, its effect is transmitting to related state and country but not so strogly globally. (Another data trend between GHG emission and temperatures does not show any correlation between the two)
- There was no distinct correlation between the emitters of greenhouse gases and green countries. One common conclusion from the global, Canada and Countries analysis is that Land Surface Temperature has been increasing for over a century and continues to do so. Albeit, at different paces. This is one of the key evidence of global warming.
- Overall, Greenhouse Gas (GHG) Emission in Canada has been decreased up to 8% at the Year 2009 from 2005 but it has continuously increased.
- Confirmed gradual increase of GHG emission over years for the provinces whose main business sector is oil and gas industry.
- On the other hand, the Province such as Ontario has experienced the steady reduction of GHG over years.
- It's hard to see clear correlation between Average surface temperature and Total GHG Emission in Canada based on the local data set in this study that is collected only in Canada during short term.
References¶
1Global Climate Change Vital Signs of the Planet, 2020: https://climate.nasa.gov/evidence/
2Greenhouse Gas sources and sinks: executive summary 2020: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/sources-sinks-executive-summary-2020.html
3The dataset link: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
4Greenhouse Gas Emissions Program: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions.html
5The dataset link: https://open.canada.ca/data/en/dataset/a8ba14b7-7f23-462a-bdbb-83b0ef629823